{"id":"W2622823936","doi":"10.1007/s10732-019-09423-y","title":"The vehicle routing problem with cross-docking and resource constraints","year":2019,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Saint-Gobain (Canada)","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Heuristics; Computer science; Mathematical optimization; DOCK; Routing (electronic design automation); Integer programming; Algorithm; Mathematics; Computer network; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001731344,0.001489407,0.002061852,0.001338044,0.0009033822,0.002989329,0.002406331,0.002870757,0.006645956],"category_scores_gemma":[0.004538298,0.001524771,0.001503983,0.002700013,0.001320541,0.003692692,0.002218056,0.001854863,0.0006396975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001585966,"about_ca_system_score_gemma":0.002083036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01230427,"about_ca_topic_score_gemma":0.008511057,"domain_scores_codex":[0.9986255,0.0006324961,0.00004938907,0.0002273261,0.0001407926,0.000324464],"domain_scores_gemma":[0.9982731,0.00121059,0.0001460047,0.00009834918,0.0001126687,0.0001592184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000876715,0.00005550511,0.0003021457,0.00007676759,0.00005523443,0.0002103782,0.00002219515,0.9665551,0.0003672089,0.02143706,0.001655203,0.009175557],"study_design_scores_gemma":[0.00003651123,0.00004993554,0.0001935166,0.00001767431,0.0000295167,0.00009872775,0.00005092019,0.9773597,0.0002800637,0.0202257,0.001639366,0.00001829782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09991249,0.001595893,0.8690974,0.001446006,0.0004792071,0.0002096284,0.0006593014,0.0002955786,0.02630445],"genre_scores_gemma":[0.7704781,0.001405913,0.2000481,0.0003888195,0.0002529673,0.0002240193,0.0005836723,0.0002407345,0.02637764],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01230427,"threshold_uncertainty_score":0.02446532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007581508004379499,"score_gpt":0.2447952879949545,"score_spread":0.237213779990575,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}